DeepSeek V3.2 vs Qwen3 32B
DeepSeek V3.2 leads Qwen3 32B on the Artificial Analysis Intelligence Index, 21.5 to 7.3, and Qwen3 32B costs 2.2x less per output token ($0.40 vs $0.88 per million). Updated October 21, 2026; 37 benchmarks have a result for both.
DeepSeek V3.2 or Qwen3 32B: which to pick
Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Qwen3 32B has none, so the two cannot be ranked on coding.
Pick Qwen3 32B for output tokens 2.2x cheaper ($0.40 vs $0.88 per million).
- Shared results
- 37
- DeepSeek V3.2 only
- 38
- Qwen3 32B only
- 8
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only DeepSeek V3.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V3.2
DeepSeek V3.2 leads, 21.5 against 7.3.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only DeepSeek V3.2 has a GPQA Diamond result, 80.3%.
Directional only
Math
AIME
No call
Only DeepSeek V3.2 has a AIME result, 84.6%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V3.2 has a MMLU Pro result, 84.9%.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3.2 has a Design Arena: websites result, 1175.
Directional only
Long documents
Listed context window
DeepSeek V3.2
DeepSeek V3.2 takes 164K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 32B
DeepSeek V3.2 $0.0007, Qwen3 32B $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V3.2 has the number, $0.99.
Directional only
At a glance
- Coding index
- DeepSeek V3.2 30.0 from 3 benchmarks, Qwen3 32B not benchmarked.
- Widest category
- Agentic work, 60.8 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): DeepSeek V3.2 73.3% against Qwen3 32B 0.0%, 73.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 $0.88 against Qwen3 32B $0.40 per million output tokens, 2.2x apart.
- Context window
- DeepSeek V3.2 takes 164K tokens, Qwen3 32B 131K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | DeepSeek V3.2 | Bars | Qwen3 32B | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 16 | 0 | ||
| All shared boards | 37 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | DeepSeek V3.2 | Qwen3 32B | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 40.0% | Only Qwen3 32B measured |
| SWE-bench Verified | 70.0% | Not measured | Only DeepSeek V3.2 measured |
| SWE-bench Multilingual | 59.0% | Not measured | Only DeepSeek V3.2 measured |
| LMArena WebDev | 1362 | Not measured | Only DeepSeek V3.2 measured |
| AA Coding Index | 44.2 | Not measured | Only DeepSeek V3.2 measured |
| LiveCodeBench (Vals AI) | 80.7% | Not measured | Only DeepSeek V3.2 measured |
| SWE-bench Verified (Vals AI) | 67.6% | Not measured | Only DeepSeek V3.2 measured |
| Vibe Code Bench (Vals AI) | 5.1% | Not measured | Only DeepSeek V3.2 measured |
| LiveCodeBench (Artificial Analysis run) | 86.2% | 54.6% | DeepSeek V3.2 |
| SciCode | Not measured | 36.0% | Only Qwen3 32B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 35.6% | 3.0% | DeepSeek V3.2 |
| Terminal-Bench | 39.6% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: coding | 1453 | 1357 | DeepSeek V3.2 |
| LMArena Text: software and IT | 1449 | 1366 | DeepSeek V3.2 |
| LMArena WebDev: React | 1350 | Not measured | Only DeepSeek V3.2 measured |
| LMArena WebDev: HTML | 1371 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.2 | Qwen3 32B | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.5 | 7.3 | DeepSeek V3.2 |
| Epoch Capabilities Index | 146.1 | 138.4 | DeepSeek V3.2 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.2 | Qwen3 32B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 90.6% | 29.8% | DeepSeek V3.2 |
| APEX-Agents | 21.3% | Not measured | Only DeepSeek V3.2 measured |
| Vending-Bench 2 | $1,034 | Not measured | Only DeepSeek V3.2 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 48.7% | Only Qwen3 32B measured |
| BrowseComp-Plus | Not measured | 10.7% | Only Qwen3 32B measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V3.2 | Qwen3 32B | Leader |
|---|---|---|---|
| GPQA Diamond | 80.3% | Not measured | Only DeepSeek V3.2 measured |
| GPQA Diamond (Artificial Analysis run) | 84.0% | 66.8% | DeepSeek V3.2 |
| Humanity's Last Exam (Artificial Analysis run) | 24.6% | 7.4% | DeepSeek V3.2 |
| GPQA Diamond (Epoch AI run) | Not measured | 65.7% | Only Qwen3 32B measured |
| ARC-AGI | 57.0% | Not measured | Only DeepSeek V3.2 measured |
| ARC-AGI-2 | 4.0% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: hard prompts | 1434 | 1334 | DeepSeek V3.2 |
| LMArena Text: expert | 1436 | 1362 | DeepSeek V3.2 |
| LMArena Text: hard prompts in English | 1446 | 1350 | DeepSeek V3.2 |
| Kagi LLM Benchmark | Not measured | 48.7% | Only Qwen3 32B measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V3.2 | Qwen3 32B | Leader |
|---|---|---|---|
| AIME | 84.6% | Not measured | Only DeepSeek V3.2 measured |
| MGSM (Vals AI) | 86.0% | Not measured | Only DeepSeek V3.2 measured |
| AA Math Index | 92.0 | 73.0 | DeepSeek V3.2 |
| AIME 2025 (Artificial Analysis run) | 92.0% | 73.0% | DeepSeek V3.2 |
| OTIS Mock AIME | Not measured | 66.9% | Only Qwen3 32B measured |
| LMArena Text: math | 1435 | 1399 | DeepSeek V3.2 |
| LMArena Text: mathematical work | 1440 | 1420 | DeepSeek V3.2 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V3.2 | Qwen3 32B | Leader |
|---|---|---|---|
| MMLU Pro | 84.9% | Not measured | Only DeepSeek V3.2 measured |
| LegalBench (Vals AI) | 76.1% | Not measured | Only DeepSeek V3.2 measured |
| CaseLaw (Vals AI) | 55.4% | Not measured | Only DeepSeek V3.2 measured |
| CorpFin (Vals AI) | 51.0% | Not measured | Only DeepSeek V3.2 measured |
| TaxEval (Vals AI) | 68.2% | Not measured | Only DeepSeek V3.2 measured |
| MedQA (Vals AI) | 93.9% | Not measured | Only DeepSeek V3.2 measured |
| MMLU Pro (Artificial Analysis run) | 86.2% | 79.8% | DeepSeek V3.2 |
| LMArena Text: science | 1446 | 1372 | DeepSeek V3.2 |
| LMArena Text: medicine and healthcare | 1440 | 1368 | DeepSeek V3.2 |
| LMArena Text: legal and government | 1425 | 1324 | DeepSeek V3.2 |
| LMArena Text: business and finance | 1410 | 1318 | DeepSeek V3.2 |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V3.2 | Qwen3 32B | Leader |
|---|---|---|---|
| Design Arena: websites | 1175 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: UI components | 1149 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: game dev | 1132 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: data viz | 1158 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: 3D | 1133 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: all code categories | 1164 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: SVG | 1032 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: ASCII art | 1083 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index160
| Benchmark | DeepSeek V3.2 | Qwen3 32B | Leader |
|---|---|---|---|
| IFBench | 60.7% | 36.3% | DeepSeek V3.2 |
| Long context reasoning (AA-LCR) | 73.3% | 0.0% | DeepSeek V3.2 |
| DTBench | 85.6% | 67.5% | DeepSeek V3.2 |
| LMCA | 28.8% | 17.3% | DeepSeek V3.2 |
| Fiction.LiveBench | Not measured | 74.2% | Only Qwen3 32B measured |
| CL-bench | 12.4% | Not measured | Only DeepSeek V3.2 measured |
| CL-bench Life | 7.4% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text | 1425 | 1340 | DeepSeek V3.2 |
| LMArena Text: instruction following | 1413 | 1305 | DeepSeek V3.2 |
| LMArena Text: creative writing | 1400 | 1297 | DeepSeek V3.2 |
| LMArena Text: English | 1439 | 1359 | DeepSeek V3.2 |
| LMArena Text: non-English | 1409 | 1317 | DeepSeek V3.2 |
| LMArena Text: Chinese | 1455 | 1357 | DeepSeek V3.2 |
| LMArena Text: German | 1413 | 1341 | DeepSeek V3.2 |
| LMArena Text: Japanese | 1374 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Korean | 1370 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: French | 1429 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Spanish | 1424 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Russian | 1424 | 1311 | DeepSeek V3.2 |
| LMArena Text: Polish | 1419 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: multi-turn | 1427 | 1331 | DeepSeek V3.2 |
| LMArena Text: longer queries | 1428 | 1327 | DeepSeek V3.2 |
| LMArena Text: writing and language | 1407 | 1298 | DeepSeek V3.2 |
| LMArena Text: entertainment and media | 1395 | 1279 | DeepSeek V3.2 |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 | Qwen3 32B | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Qwen3 32B costs less
Repository review
50K input + 3K output tokens
Qwen3 32B costs less
Agent loop
200K input + 10K output tokens
Qwen3 32B costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | DeepSeek V3.2 | Qwen3 32B |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | Open weights |
| Released | December 1, 2025 | April 28, 2025 |
| Context window | 164K | 131K |
| Input $/Mtok | $0.29 | $0.14 |
| Output $/Mtok | $0.88 | $0.40 |
| Coding index | 30.0 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.99 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 better than Qwen3 32B for coding?
Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Qwen3 32B has none, so the two cannot be ranked on coding. Pick Qwen3 32B for output tokens 2.2x cheaper ($0.40 vs $0.88 per million).
Which is cheaper, DeepSeek V3.2 or Qwen3 32B?
DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; Qwen3 32B costs $0.14 and $0.40. Cost per solved task is known for DeepSeek V3.2 only.
Which has the bigger context window, DeepSeek V3.2 or Qwen3 32B?
DeepSeek V3.2, with 164K tokens against 131K.
How many benchmarks is the DeepSeek V3.2 vs Qwen3 32B comparison based on?
37 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2's coding index rests on 3 independent benchmarks and Qwen3 32B's on 0. Where only one model has a result, the page says so instead of naming a winner.